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The Official Practice Question Set: AWS Certified AI Practitioner (AIF-C01) consists of 20 questions. This question set aligns with the AIF-C01 version of the exam and exam guide. If you are looking for an assessment that is the same length as the…

The Official Practice Question Set: AWS Certified Machine Learning - Specialty (MLS-C01) consists of 20 questions. This question set aligns with the MLS-C01 version of the exam and exam guide. Official Practice Question Sets feature 20 questions…

The Official Practice Question Set: AWS Certified Machine Learning Engineer - Associate (MLA-C01) consists of 20 questions. This question set aligns with the MLA-C01 version of the exam and exam guide. If you are looking for an assessment that is…

Machine learning (ML) and Artificial Intelligence (AI) are becoming mainstream. In this course, you will spend a day in the life of a data scientist so that you can collaborate efficiently with data scientists and build applications that integrate…

This course was developed by members of AWS Technical Field Communities (TFC), an AWS community of technical experts. The content is intended to complement our standard training curriculum and augment your AWS learning journey. We are aware some…

Learn the full GenAIOps lifecycle for generative AI applications, from planning and prompt management to evaluation, automated testing, monitoring, and tracing in production.

Modules in this learning path:

• Plan Prepare Genaiops
• Prompt Versioning Genaiops
• Evaluate Optimize Agents
• Automated Evaluation Genaiops
• Monitor Generative AI App
• Tracing Generative AI App

Products: Foundry Tools, Microsoft Foundry, Github

Roles: Data Scientist, AI Engineer, Devops Engineer

Level: Intermediate

Subjects: Artificial Intelligence, Machine Learning, Natural Language Processing, Devops

Duration: 364 minutes

Learn how to design, train, optimize, automate, deploy, and monitor machine learning models with Azure Machine Learning and GitHub Actions.

Modules in this learning path:

• Design Machine Learning Model Training Solution
• Experiment Azure Machine Learning
• Run Training Scripts Track Models Mlflow
• Perform Hyperparameter Tuning Azure Machine Learning Pipelines
• Run Pipelines Azure Machine Learning
• Trigger Azure Machine Learn Jobs Github Actions
• Deploy Model Github Actions

Products: Azure Machine Learning, Github

Roles: Data Scientist

Level: Intermediate

Subjects: Artificial Intelligence, Machine Learning

Duration: 344 minutes

Learn how to select fine-tuning methods (SFT, RFT, DPO), recognize agent quality problems, prepare training data, and design optimization strategies using hyperparameter configuration and iterative evaluation.

Units in this learning path:

• Introduction
• Select Finetune Method
• Scenarios
• Prepare Data
• Optimize Finetune
• Exercise
• Knowledge Check
• Summary

Products: Microsoft Foundry

Roles: Data Scientist, AI Engineer

Level: Intermediate

Subjects: Artificial Intelligence, Machine Learning, Natural Language Processing

Duration: 89 minutes

Learn how to optimize business processes using Microsoft Copilot.

Modules in this learning path:

• Unlock Productivity Unleash Creativity AI Powered Chat
• Draft Impactful Documents Using AI
• Present Copilot Microsoft Powerpoint
• From Inbox Impact Improve Your Email Workflows AI
• Uncover New Data Insights AI

Products: M365, Office 365

Roles: Business User

Level: Beginner

Subjects: Artificial Intelligence, Business Applications, Compliance, Productivity, Security

Duration: 193 minutes

Explore complementary strategies to optimize generative AI model performance, including prompt engineering, system messages, model parameters, Retrieval Augmented Generation (RAG), and fine-tuning. Learn when to use each strategy and how to combine them.

Units in this learning path:

• Introduction
• Prompt Engineering
• Retrieval Augmented Generation
• Fine Tune Model
• Compare Combine Strategies
• Exercise
• Knowledge Check
• Summary

Products: Microsoft Foundry

Roles: Data Scientist, AI Engineer

Level: Intermediate

Subjects: Artificial Intelligence, Machine Learning, Natural Language Processing

Duration: 131 minutes

Optimize multi-agent performance and cost in Microsoft Foundry. Design model routing strategies across agent ecosystems, implement multi-level caching architectures, optimize token usage and context management across agent chains, and systematically analyze quality-cost-latency trade-offs.

Units in this learning path:

• Introduction
• Design Model Route Agent Ecosystems
• Implement Multi Level Cache Strategies
• Optimize Token Usage Context Management
• Balance Quality Cost Latency Tradeoffs
• Knowledge Check
• Summary

Products: Azure, Microsoft Foundry

Roles: AI Engineer, Solution Architect

Level: Advanced

Subjects: Artificial Intelligence, Machine Learning

Duration: 40 minutes

In this course, you will explore two techniques to improve the performance of a foundation model (FM): Retrieval Augmented Generation (RAG) and fine-tuning. You will learn about Amazon Web Services (AWS) services that help store embeddings with…